Abstract
Preeclampsia (PE) is a pregnancy-specific complication that typically occurs in the mid to late stages of gestation. Its main clinical features include gestational hypertension, proteinuria, and multiple organ dysfunction. In severe cases, it may progress to eclampsia, HELLP syndrome, and even pose life-threatening risks to both mother and fetus. Recent studies have found that serum uric acid (UA) levels are significantly elevated in patients with PE and closely correlated with disease severity, suggesting that UA may not only serve as a metabolic marker but also play an active role in the pathogenesis of PE. At the same time, coagulation dysfunction-an important pathological feature of PE-manifests as thrombocytopenia, consumption of coagulation factors, suppression of the fibrinolytic system, and microthrombus formation. These changes contribute significantly to organ damage and adverse pregnancy outcomes in PE. This review summarizes the dynamic changes in serum uric acid levels during PE and explores their relationship with coagulation abnormalities. Particular emphasis is placed on the potential mechanisms by which uric acid induces coagulation disorders, including endothelial cell injury, oxidative stress aggravation, and activation of inflammatory pathways. Additionally, the review discusses the clinical utility of various coagulation-related biomarkers (such as D-dimer, fibrinogen, PAI-1, and P-selectin) in the early prediction, severity assessment, and clinical management of PE. Current research indicates that combined monitoring of serum uric acid and coagulation markers-for instance, models integrating UA with the sFlt-1/PlGF ratio or with PAI-1, which have demonstrated high predictive accuracy (AUC >0.90) for early-onset PE-may improve early detection and risk stratification. However, the current evidence remains primarily observational and is limited by heterogeneity in study designs. Future studies should prioritize well-designed prospective cohorts to clarify causal relationships and explore more sensitive and specific combined predictive models to provide a stronger theoretical and clinical foundation for improving maternal and fetal outcomes.